A machine learning approach to 3D protein structure sonification
| dc.contributor.author | Ronan, Isabel | en |
| dc.contributor.author | Mi, Yanlin | en |
| dc.contributor.author | Yallapragada, Venkata Vamsi Bharadwaj | en |
| dc.contributor.author | Ó Nuanáin, Cárthach | en |
| dc.contributor.author | Tabirca, Sabin | en |
| dc.contributor.funder | Science Foundation Ireland | en |
| dc.contributor.funder | Munster Technological University | en |
| dc.date.accessioned | 2025-02-19T16:39:19Z | |
| dc.date.available | 2025-02-19T16:39:19Z | |
| dc.date.issued | 2024-01 | en |
| dc.description.abstract | Proteins are intricate structures that can be analysed by biologists and presented to the public using visualisations. However, with an increase in the amount of readily available protein-related information, new forms of data representation are needed. Sonification offers multiple advantages when conveying large amounts of complex data to interested audiences. Previous attempts have been made to sonify protein data; these techniques mainly focus on using amino acid sequences and secondary structures. This paper proposes a novel protein sonification algorithm involving atomic coordinates, B-factors, and occupancies to investigate new ways of displaying 3D protein structure data. This study culminates in creating a cultural showcase involving some of nature's most significant molecular structures. Results of both musical analysis and the showcase indicate that protein sonification has the potential to act as a helpful outreach and engagement tool for biologists while also helping experts in the field glean new insights from complex data. | en |
| dc.description.sponsorship | Munster Technological University (Create Le Chéile Arts Project Award) | en |
| dc.description.status | Peer reviewed | en |
| dc.description.version | Published Version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Ronan, I., Mi, Y., Yallapragada, V. V. B., Ó Nuanáin, C. and Tabirca, S. (2024) 'A machine learning approach to 3D protein structure sonification', International Journal of Music Science, Technology and Art, 6(1), pp. 9-18. https://doi.org/10.48293/IJMSTA-106 | en |
| dc.identifier.doi | https://doi.org/10.48293/IJMSTA-106 | en |
| dc.identifier.endpage | 18 | en |
| dc.identifier.issn | 2612-2146 | en |
| dc.identifier.issued | 1 | en |
| dc.identifier.journaltitle | International Journal of Music Science, Technology and Art | en |
| dc.identifier.startpage | 9 | en |
| dc.identifier.uri | https://hdl.handle.net/10468/17084 | |
| dc.identifier.volume | 6 | en |
| dc.language.iso | en | en |
| dc.publisher | Studio Musica Press | en |
| dc.relation.project | info:eu-repo/grantAgreement/SFI/SFI Centres for Research Training Programme::Data and ICT Skills for the Future/18/CRT/6222/IE/SFI Centre for Research Training in Advanced Networks for Sustainable Societies/ | en |
| dc.rights | © 2024, the Authors. Licensed to IJMSTA. This is an open access article distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited. | en |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | Machine learning | en |
| dc.subject | KNN | en |
| dc.subject | Protein | en |
| dc.subject | Sonification | en |
| dc.subject | Music | en |
| dc.title | A machine learning approach to 3D protein structure sonification | en |
| dc.type | Article (peer-reviewed) | en |
